1. Field of the Invention
The present invention relates to an apparatus and method for performing channel estimation in a mobile communication system, and more particularly to an apparatus and method for performing adaptive channel estimation according to wireless channel environments in a mobile communication system.
2. Description of the Related Art
Mobile communication systems have evolved from providing a user with voice signals to providing the user with high-speed and high-quality wireless data packets, allowing users to use a variety of data services and multimedia services. A third-generation mobile communication system classified as a 3GPP (3rd Generation Partnership Project) is an asynchronous system, and a 3GPP2 is a synchronous system. Both 3rd generation systems are being standardized to implement high-speed and high-quality wireless packet communication services. For example, a HSDPA (High Speed Downlink Packet Access) standardization is in progress for the 3GPP, and a 1×EV-DV standardization is in progress for the 3GPP2. Standardization is needed for users or subscribers to receive high-speed (more than 2 Mbps) and high-quality wireless data packet transmission service in the third-generation mobile communication system. A fourth-generation mobile communication system is needed for users or subscribers to receive higher-speed and higher-quality multimedia communication services.
Typically, the HSDPA scheme is a specific data transfer scheme for processing a HS-DSCH (High Speed-Downlink Shared Channel) and its associated control channels. The HS-DSCH is a downlink data channel for supporting a downlink high-speed packet data transmission service in an asynchronous UMTS (Universal Mobile Terrestrial System) mobile communication system.
The HSDPA scheme requires general techniques used for a conventional mobile communication system, and other advanced techniques for improving channel environment adaptability. Recently, three schemes have been developed for supporting a high-speed packet transmission service in the HSDPA, i.e., an AMCS (Adaptive Modulation and Coding Scheme), a n-channel SAW (Stop And Wait) HARQ (Hybrid Automatic Repeat Request) scheme, and a FCS (Fast Cell Selection) scheme.
Firstly, the AMCS determines modulation and coding methods of a data channel according to a channel status between a cell and a user. This results in increased channel usage efficiency of overall cells. A combined scheme between the modulation method and the coding method is called a MCS (Modulation and Coding Scheme), and can be defined as a plurality of MCSs ranging from a level “1” to a level “n”. The AMCS adaptively determines individual levels of the MCSs according to a channel status between a user and a cell. This results in increased overall channel usage efficiency.
Secondly, the n-channel SAW HARQ scheme functioning as one of many HARQ schemes successively transmits a plurality of packets even though an ACK (ACKnowledgement) signal is not received, resulting in increased channel usage efficiency. In other words, provided that N logic channels are set up between a UE (User Equipment) and a Node-B, and these N logic channels can be identified by a specific time or a specific channel number, the UE serving as a reception end can recognize which one of the channels contains a packet received at a predetermined time. Further, the UE can reconstruct packets in the order of reception.
Thirdly, the FCS scheme receives packets from a cell which maintains the best channel status when the UE using the HSDPA enters a soft handover region. This results in reduced overall interference between channels. If the cell for providing a user with the best channel status is changed to a new cell, the FCS scheme performs packet transmission using a HS-DSCH of this new cell. When performing such packet transmission, there is a need for the FCS scheme to minimize transmission discontinuity time.
The AMCS contained in the aforementioned three high-speed packet transmission services will hereinafter be described in more detail.
There are a variety of modulation/demodulation schemes being currently investigated for the AMCS, for example, a QPSK (Quadrature Phase Shift Keying), a 8PSK, and a 16QAM (16 Quadrature Amplitude Modulation), etc. A variety of code rates ranging from “¼” to “1” are being considered by many developers as a coding scheme. Therefore, a mobile communication system using the AMCS provides high-order modulation/demodulation schemes (e.g., 8PSK, and 16QAM) and a high code rate to UEs (e.g., UEs located in the vicinity of a Node-B) assigned to a good channel, whereas it provides a low-order modulation scheme such as a QPSK and a low code rate to UEs (e.g., UEs located at a cell boundary) assigned to a relatively poor channel. Rather than use the QPSK, it is possible for the QPSK serving as a low-order modulation scheme to perform channel estimation using a phase prediction function, because the QPSK contains one symbol for every quadrant with respect to the constellation. However, two or four symbols for every quadrant are provided for the 8PSK and 16QAM serving as a high-order modulation scheme. Specifically, several symbols having different amplitudes are positioned in the same phase region, such that not only phase estimation but also channel estimation based on precise amplitude information is needed.
In the meantime, the utilization of the high-order modulation scheme for performing high-speed and high-quality data services and the high code rate is mainly restricted due to a variety of wireless channel environments, for example, a white noise, a variation in signal reception power levels due to the fading phenomenon, a shadowing occurrence, a Doppler effect caused by UE's mobility and UE's frequent speed change, and a signal interference caused by either another user or multipath signals, etc. Therefore, a mobile communication system requires appropriate modulation/coding schemes according to wireless channel environments which vary according to the above mentioned factors. A receiver of the mobile communication system should have a channel estimator functioning as an additional signal compensator for changing reception signals undesirably distorted by the above mentioned factors to original signals.
Typically, a conventional mobile communication system adapts pilot signals to predict such wireless channel environments. Specifically, a Node-B transmits pilot signals over a common pilot channel such as a PICH or CPICH. All UEs contained in a given area of the Node-B receive the pilot signals from the Node-B, and predict the wireless channel environments such as the fading phenomenon using the received pilot signals. However, it is difficult for the above method to predict a wireless channel environment change caused by random white noise characteristics. To solve this problem, the channel estimator includes a noise elimination filter for smoothing random characteristics of the white noise, such that the noise can be considerably reduced. For example, IIR (Infinite Impulse Response) filter is mainly adapted as the noise elimination filter, and is suitable for a mobile communication system adapting a QPSK as a modulation scheme.
FIG. 1 is a block diagram of an example of a channel estimator for use in a conventional mobile communication system. FIG. 2 is a block diagram illustrating an example of a channel estimator for use in a receiver of a conventional mobile communication system. The channel estimator shown in FIG. 1 includes a first integration/dump filter 110, a second integration/dump filter 130, a complex conjugate pattern generator 120, and a noise elimination filter 140.
Referring to FIG. 1, input signals IN are provided to the first integration/dump filter 110. The input signals may be pilot signals received from common pilot channels, for example. The first integration/dump filter 110 accumulates the input signals (i.e., the pilot signals) in response to a SF (Spreading Factor) used for the common pilot channels, and numerically integrates the accumulated input signals. A measurement detected by the first integration/dump filter 110 may be reception intensity in symbol units in association with the input signals. This measurement is provided to a multiplier 150.
The complex conjugate pattern generator 120 generates a complex conjugate pattern corresponding to a symbol pattern of a pilot signal transmitted over the common pilot channel. The complex conjugate pattern generated from the complex conjugate pattern generator 120 is applied to the multiplier 150. The multiplier 150 multiplies the complex conjugate pattern generated from the complex conjugate pattern generator 120 by the measurement generated from the first integration/dump filter 110, and thus generates a subdivided signal corresponding to a desired antenna. The subdivided signal associated with the desired antenna is received from the multiplier 150, and is transmitted to the second integration/dump filter 130. The second integration/dump filter 130 receives subdivided signals for desired antennas from the multiplier 150, accumulates the subdivided signals in two-symbol units, numerically integrates the accumulated signals, and thus outputs a channel prediction value. But, this channel prediction value does not consider the white noise contained in the input signals IN. Therefore, the channel prediction value is transmitted to the noise elimination filter 140 to obtain a more accurate value which considers the white noise. The noise elimination filter 140 then removes the white noise component contained in the channel prediction value from an output signal of the second integration/dump filter 130, and thus generates a correct channel prediction value. The IIR filter may be adapted as the noise elimination filter 140.
A detailed circuit diagram of the IIR filter functioning as the noise elimination filter 130 is shown in FIGS. 2 and 3. FIG. 3 is a detailed block diagram of an example of an N-th IIR filter adapted as another example of the noise elimination filter of FIG. 1. Specifically, FIG. 2 is a detailed circuit diagram of a primary IIR filter, and FIG. 3 is a detailed circuit diagram of an N-th IIR filter.
I/O (Input/Output) characteristic of the primary IIR filter is represented by the following Equation 1:y(n)=b·x(n)+a·y(n−1)  [Equation]
where y(n) is a current output signal, x(n) is a current input signal, and y(n−1) is a previous output signal.
With reference to the above Equation 1, the output signal “y(n)” is the sum of the input signal “x(n)” multiplied by a constant “b” and one-delayed output signal “y(n−1)” multiplied by the other constant “a”.
The I/O characteristic of the N-th IIR filter is represented by the following Equation 2:
                              y          ⁡                      (            n            )                          =                                            b              ⁢              •                        ⁢                                                  ⁢                          x              ⁡                              (                n                )                                              +                                    ∑                              k                =                1                            N                        ⁢                                          a                k                            ⁢                              •y                ⁡                                  (                                      n                    -                    k                                    )                                                                                        [Equation  2]            
As can be seen from the above Equation 2, the I/O characteristic of the N-th IIR filter considers first to N-th previous output signals y(n−1), y(n−2), . . . , y(n−N). As shown in the above Equation 2, the output signal “y(n)” is the sum of the input signal “x(n)” multiplied by a constant “b” and individual delayed output signals (i.e., first to N-th delayed output signals) multiplied by the other constant “a”.
As can be seen from the above Equations 1 and 2, the characteristic of the IIR filter is determined by filter coefficients “a” and “b”. The filter coefficient “a” is a feedback weight, and the other coefficient “b” is an input weight.
Referring to FIG. 2, an input signal x(n) is provided to a first multiplier 210. The signal is multiplied by the first filter coefficient “b”, and is then transmitted to one input terminal of an adder 212. The other input terminal of the adder 212 receives a signal a·y(n−1) derived by multiplication of a previous output signal y(n−1) and a second filter coefficient “a”. The adder 212 adds an output signal b·x(n) of the first multiplier 210 and the output signal a·y(n−1) of a second multiplier 216 to create a result signal b·x(n)+a·y(n−1), resulting in a channel prediction value y(n) equal to the result signal b·x(n)+a·y(n−1). The channel prediction value y(n) is transmitted to a delay 214. The delay 214 delays the received signal y(n) to provide a delayed signal y(n−1), and transmits the delayed signal y(n−1) to the second multiplier 216.
The IIR filter applies a previous output signal to a new input signal, and thus prevents its output signal from being abruptly changed due to a white noise.
The frequency characteristic of the primary IIR filter shown in FIG. 2 is represented by the following Equation 3:
                              H          ⁡                      (                          ⅇ                              j                ⁢                                                                  ⁢                w                                      )                          =                  b                      1            -                          a              ⁢                                                          ⁢                              •ⅇ                                                      -                    j                                    ⁢                                                                          ⁢                  w                                                                                        [Equation  3]            
A direct current (DC) gain of the primary IIR filter having the above frequency characteristic shown in the above Equation 3 is a specific value at a prescribed condition of ω=0 indicating a frequency of “0”, such that the DC gain can be represented by the following Equation 4:
                                                    H            ⁡                          (              1              )                                                =                                          b                                                                      1              -              a                                                                      [Equation  4]            
Therefore, with reference to the above Equation 4, the DC gain at a predetermined condition of |b|=|1−a| is normalized to “1”.
Referring to FIG. 3, an input signal x(n) is applied to a first multiplier 310. The input signal is multiplied by the first filter coefficient “b”, and is then applied to an adder 312. The other input terminal of the adder 312 receives signals a1·y(n−1) to aN·y(n−N) from N second multipliers 316-1 to 316-N. The received signals a1·y(n−1) to aN·y(n−N) are created by a multiplication previous output signals y(n−1) to y(n−N) and second filter coefficients a1 to aN. The adder 312 adds an output signal b·x(n) of the first multiplier 310 and the output signals a1·y(n−1) to aN·y(n−N) of the second multipliers 316 to create a result signal b·x(n)+a1·y(n−1)+ . . . +aN·y(n−N), resulting in a channel prediction value y(n) equal to the result signal b·x(n)+a1·y(n−1)+ . . . +aN·y(n−N). The channel prediction value y(n) is transmitted to a plurality of delays 314-1 to 314-N to create delayed signals y(n−1), y(n−2), . . . , y(n−N). In this case, the delays 314-1 to 314-N have different delay values.
As stated above, the N-th IIR filter shown in FIG. 3 uses N feedback signals composed of first to N-th feedback signals, resulting in more precisely correcting a noise component contained in a desired signal.
The frequency characteristic of the N-th IIR filter shown in FIG. 3 is represented by the following Equation 5:
                              H          ⁡                      (                          ⅇ                              j                ⁢                                                                  ⁢                w                                      )                          =                                            Y              ⁡                              (                                  ⅇ                                      j                    ⁢                                                                                  ⁢                    w                                                  )                                                    X              ⁡                              (                                  ⅇ                                      j                    ⁢                                                                                  ⁢                    w                                                  )                                              =                      b                          1              -                                                ∑                                      k                    =                    1                                    N                                ⁢                                                      a                    k                                    ⁢                                      •ⅇ                                                                  -                        j                                            ⁢                                                                                          ⁢                      w                                                                                                                              [Equation  5]            
A DC gain of the N-th IIR filter having the above frequency characteristic shown in the above Equation 5 is represented by the following Equation 6:
                                                    H            ⁡                          (              1              )                                                =                                          b                                                                      1              -                                                ∑                                      k                    =                    1                                    N                                ⁢                                  a                  k                                                                                                    [Equation  6]            
Therefore, with reference to the above Equation 6, the DC gain at a predetermined condition of
          b        =                1      -                        ∑                      k            =            1                    N                ⁢                  a          k                        is normalized to “1”.
The channel estimator can be implemented with a plurality of channel estimators according to whether or not a Tx-diversity (i.e., a transmission diversity) is used. If the Tx-diversity is not used, the channel estimator can be implemented with only one channel estimator. Otherwise, if the Tx-diversity is used, a plurality of channel estimators equivalent to the number of antennas used are needed. However, the channel estimator has the same configuration as FIG. 1, irrespective of the use of Tx-diversity. Referring to FIG. 1, a complex conjugate pattern generated from the complex conjugate pattern generator 120 contained in the channel estimator may be one or more patterns according to the use of Tx-diversity. If Tx-diversity is provided, only one antenna is used, such that only one symbol pattern is generated. If Tx-diversity is adapted using a plurality of antennas, a plurality of symbol patterns are adapted to discriminate among the plurality of antennas. The symbol patterns are adapted to discriminate among the antennas so as to separate each pilot signal from orthogonal pilot signals for every antenna, allowing individual symbol patterns associated with individual antennas to be orthogonal to each other.
In another example, individual channel estimators should be configured to be associated with individual antennas, one channel estimator selected from among many channel estimators should always be operated, irrespective of the use of Tx-diversity. The remaining channel estimators other than the selected one channel estimator should be operated only when they use the Tx-diversity.
Although a channel estimator having the IIR filter is suitably used for a QPSK being a low-order modulation scheme, several problems may occur if a high-order modulation scheme is applied to the channel estimator. For example, a 16QAM being a high-order modulation scheme is very sensitive to a noise problem as compared to QPSK, such that the 16QAM is mainly used at a relatively high SNR (Signal to Noise Ratio). In conclusion, there is less necessity for the IIR filter in the 16QAM compared with the QPSK. In more detail, a noise elimination filter may deteriorate performance of the channel estimator in a wireless channel environment where there is a white noise lower than that of the QPSK. This problem of the noise elimination filter is called a lagging phenomenon, and is caused by characteristics of the IIR filter.
The following Table 1 shows examples of preferable coefficient values “a” and “b” which are adapted to the IIR filter according to the Doppler frequency and a moving speed of a mobile terminal (i.e., a UE). As shown in the Table 1, it is assumed that a chip rate is 3.84 Mcps, and a sample frequency fs, for updating channel estimation in units of 516 chips is 3.84 Mcps/512 chip, i.e., 7500 Hz.
TABLE 1Ab = 1 − aCutoff frequency (3 dB)Transfer speed1/43/42024 Hz1093 km/h1/21/2 862 Hz 465 km/h3/41/4 346 Hz 197 km/h7/81/8 159 Hz 86 km/h
Typically, a mobile communication system determines a moving speed of a mobile terminal, and sets the filter coefficients “a” and “b” associated with the determined moving speed to fixed values, respectively. Specifically, the filter coefficients “a” and “b” for use in the IIR filter are respectively fixed to only one value corresponding to a specific moving speed of the mobile terminal, such that the IIR filter can be operated by the fixed values “a” and “b” even though a wireless channel environment is changed to another wireless channel environment, resulting in unexpected problems.
Such unexpected problems will hereinafter be described in more detail with reference to FIGS. 4a and 4b. A feedback signal (i.e., a previous output signal) of the IIR filter shown in FIGS. 2 and 3 incurs the lagging phenomenon, because a current output signal of the IIR filter is based on a previous output signal. The lagging phenomenon is more critical to a wireless channel environment changing at a high speed, and is more clearly shown in FIGS. 4a and 4b. FIG. 4a is a graph illustrating an example of a lagging phenomenon of the IIR filter in a conventional high-speed fading channel. FIG. 4b is a graph illustrating an example of a lagging phenomenon of the IIR filter in a conventional low-speed fading channel. As can be seen from FIGS. 4a and 4b, the lagging phenomenon of FIG. 4a has a signal level higher than that of FIG. 4b. Therefore, provided that a wireless channel environment is abruptly changed, there are large differences in predicted values of a channel estimator even though the same delay time is provided, such that it is impossible for a reception signal to further reduce its own BER (Bit Error Rate).
To overcome the aforementioned disadvantages, the low-order modulation scheme is generally operated at a region of a low SNR lower than that of the high-order modulation scheme, and is more sensitive to a signal distortion than a signal amplitude, such that it is not affected by the lagging phenomenon. However, if a high-order modulation scheme is used in a same way as in a HSDPA mobile communication system, the lagging phenomenon deteriorates the overall system performance. Therefore, a new method for solving the above problems is needed.